Azure Data Architect

Job Summary

The Azure Data Solutions Architect leads the design and implementation of enterprise-scale data solutions using Azure Data Factory, Azure Databricks, and Azure Synapse Analytics. This role provides architectural leadership, ensuring solutions are robust, scalable, and aligned with business strategy and industry best practices. The architect drives innovation, enforces governance, and mentors teams to deliver high-quality, future-ready data platforms that support organizational transformation.

Key Responsibilities

Design and implement scalable data architectures using Azure services

Build and optimize ETL/ELT pipelines using Azure Databricks and Apache Spark

Architect data lake and data warehouse solutions using Azure Data Lake and Azure Synapse Analytics

Define data modeling strategies (batch & real-time processing)

Ensure data security, governance, and compliance standards

Collaborate with stakeholders to understand business requirements and translate them into technical solutions

Optimize performance and cost efficiency of data workloads

Implement CI/CD pipelines for data engineering workflows

Mentor and guide data engineers and development teams

Integrate advanced analytics, AI, and ML solutions when required

Skill Requirements

Strong experience with Azure Databricks and Spark (PySpark/Scala)

Hands-on experience with Azure services (ADF, ADLS, Synapse, Event Hub)

Expertise in big data architecture and distributed systems

Strong knowledge of SQL, Python, and data engineering concepts

Experience with data modeling techniques (star schema, dimensional modeling)

Understanding of real-time streaming (Kafka/Event Hub)

Knowledge of DevOps and CI/CD practices

Strong Development Area:

Data Engineering Foundations

Batch vs streaming; lakehouse concepts; medallion (Bronze/Silver/Gold); file formats (Parquet/Delta/CSV/JSON/Avro); partitioning & clustering; schema evolution; data governance basics; DevOps/CI-CD for data.

SQL

Joins, subqueries, CTEs, window functions, set operations; aggregation & rollups; MERGE/UPSERT; analytic functions; performance (indexes, partition pruning, statistics); data validation scenarios (dedupe, top-N, SCD keys).

Apache Spark (Core)

Spark architecture (driver/executors), DAG, stages/tasks; RDD vs DataFrame/Dataset; wide vs narrow transformations; shuffle mechanics; caching/persistence; partitioning; broadcast joins; skew handling; checkpointing; job tuning.

PySpark

DataFrame API, Spark SQL; UDF vs pandas UDF; windowing; incremental loads; structured streaming (triggers, watermarks); handling semi-structured data; optimizing with predicates, pushdown, join strategies; error handling; unit testing (pytest + chispa).

Databricks Platform

Workspace basics; clusters (Single Node/All-Purpose/Jobs), cluster policies; DBR/LTS; notebooks & Repos; Jobs & Workflows; Delta Lake & Delta Live Tables; Unity Catalog (catalog/schema/table, permissions, lineage); MLflow basics; secret scopes; DBFS; REST APIs; Databricks Connect.

Delta Lake / Lakehouse Patterns

ACID transactions; schema enforcement/evolution; time travel; OPTIMIZE/ZORDER; VACUUM; CDC patterns (MERGE INTO, change data feed); streaming vs batch Delta; expectations/constraints; table maintenance strategies.

Orchestration & Scheduling

Databricks Workflows; Azure Data Factory/Synapse pipelines; triggers; parameter passing; fail/retry; alerts; integration with AutoSys/Control-M/Jenkins/GitHub Actions; event-driven patterns.

Python (Core for Data)

Core syntax; typing & data structures; file I/O; logging; virtual environments; packaging; testing (pytest); common data libs (pandas, pyarrow); error handling; performance considerations (vectorization, generators).

Data Quality & Testing

Great Expectations/dbx expectations or custom checks; unit/integration tests; reconciliation (row/amount); anomaly detection; contract testing for schemas; data observability (metrics, SLAs, freshness).

Security & Governance

Unity Catalog permissions, row/column-level security; secrets management (Key Vault/Secret scopes); PII handling; audit logs; token management; compliance basics.

Cost & Performance Optimization

Cluster sizing, autoscaling; DBU awareness; spot/preemptible instances; storage formats; caching; efficient joins; job scheduling; monitoring with metrics & Ganglia/Spark UI; cost tagging and chargeback.

Analytical Skills & Problem Solving

Break down data problems; root-cause incidents; propose alternatives; estimate complexity; communicate clearly with stakeholders.

Stake Holder Management

Interaction with Client Stakeholders, Communication.

Other Requirements

1. Microsoft Certified: Azure Solutions Architect Expert (Recommended)

2. Microsoft Certified: Azure Data Engineer Associate (Optional But Valuable